{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x238f8da7cc8>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([3,5,7,6,2,6,10,15])\n",
    "plt.plot(x,y,'r')  ##绘制折线图 args1 x args2 y args3 color\n",
    "## 3大图表 折线，饼状，柱状\n",
    "plt.plot(x,y,'g',lw=10) #args4 折线宽度 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kgu7v6R4Wf0c1Ns6KAk9yL/AwcHmSk0luHXWmZa4FPsDiTHLpUqr3jDoUMAl8Ocm3WXzfm6NVNVaX7I2hHcDXknwL+Drwr1X1hRFnWu5DwD3d/9PdwN+NNs6iJBcAe1mc4Y6N7qeV+4FHge+w2JdjdVv9WXEZoSS9Fp0VM3BJei2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1Kj/h/UArrHtjoYqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([13,15,71,16,12,6,10,15])\n",
    "plt.bar(x,y,0.9,alpha=1,color='b')  # args3 柱宽度比例， args4 透明度，  args5 color\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
